Multiple Regression-Based Dynamic Amplification Factor Investigation of Monorail Tourism Transit Systems

The monorail tourism transit system (MTTS) is a large-scale amusement facility. Currently, there is limited theoretical research on the vehicle–bridge coupling vibration and dynamic amplification factor (DAFs) of this system. The values specified in relevant standards are not entirely reasonable; for instance, the calculated value of the DAFs in the “Large-scale amusement device safety code (GB 8408-2018)” only takes speed into account and is set at 0.44 when the speed is between 20 and 40 km/h. This is overly simplistic and obviously too large. This paper aims to establish a reasonable expression of the DAFs for the MTTS and improve the design code of the industry. Firstly, using on-site trials of the project and the dynamics numerical simulation method, the dynamic response characteristics of the MTTS and the influencing factors of the DAFs were systematically analyzed. The rationality and accuracy of the model were verified. Secondly, combined with the joint simulation model, the dynamic influence mechanism of multifactor coupling on the DAFs was revealed. On this basis, the key regression parameters were selected by using the Pearson correlation coefficient method and the random forest algorithm, and the DAFs prediction model was constructed based on the least absolute shrinkage and selection operator (LASSO) regression theory. Finally, through cross-comparison of simulation data and specification verification, a recommended calculation expression of the DAFs for the MTTS was proposed. The research results show that the established prediction model can predict 94.50% of the variation information of the DAFs of the MTTS and pass the 95% confidence level and 0.05 significance test. The accuracy is high and relatively reasonable and can provide a reference for the design of the MTTS.

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Publication Details

Journal
Buildings
Published
2025-05-29
DOI
https://doi.org/10.3390/buildings15111881
Primary Topic
Traffic Prediction and Management Techniques
Type
article
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article

Multiple Regression-Based Dynamic Amplification Factor Investigation of Monorail Tourism Transit Systems

Shiqi Wei, Changxing Wu, Wenlong Liu, Yonggang Wang et al.
Buildings
Traffic Prediction and Management Techniques
article

Multiple Regression-Based Dynamic Amplification Factor Investigation of Monorail Tourism Transit Systems

Shiqi Wei, Changxing Wu, Wenlong Liu, Yonggang Wang, Hong Zhang
article en

Abstract

The monorail tourism transit system (MTTS) is a large-scale amusement facility. Currently, there is limited theoretical research on the vehicle–bridge coupling vibration and dynamic amplification factor (DAFs) of this system. The values specified in relevant standards are not entirely reasonable; for instance, the calculated value of the DAFs in the “Large-scale amusement device safety code (GB 8408-2018)” only takes speed into account and is set at 0.44 when the speed is between 20 and 40 km/h. This is overly simplistic and obviously too large. This paper aims to establish a reasonable expression of the DAFs for the MTTS and improve the design code of the industry. Firstly, using on-site trials of the project and the dynamics numerical simulation method, the dynamic response characteristics of the MTTS and the influencing factors of the DAFs were systematically analyzed. The rationality and accuracy of the model were verified. Secondly, combined with the joint simulation model, the dynamic influence mechanism of multifactor coupling on the DAFs was revealed. On this basis, the key regression parameters were selected by using the Pearson correlation coefficient method and the random forest algorithm, and the DAFs prediction model was constructed based on the least absolute shrinkage and selection operator (LASSO) regression theory. Finally, through cross-comparison of simulation data and specification verification, a recommended calculation expression of the DAFs for the MTTS was proposed. The research results show that the established prediction model can predict 94.50% of the variation information of the DAFs of the MTTS and pass the 95% confidence level and 0.05 significance test. The accuracy is high and relatively reasonable and can provide a reference for the design of the MTTS.

BuildingsVol. 15(11)
Central South University of Forestry and Technology (CN), Central South University (CN)
Openalex Percentile: Top 88%
Traffic Prediction and Management Techniques
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